What’s new#

Version 0.1.8 (15/07/2025)#

Correct license to LGPL v2.1 or later.
Bump dependencies to cffi>=1.17.1 and setuptools>=77.0.3.
eaf(), vorob_t() and vorob_dev() take the set indices as a separate argument sets following the API of the R package.
New example Empirical Attainment Function.
Document EAF and Vorob’ev expectation and deviation in more detail.
New online dataset: DTLZLinearShape.8d.front.60pts.10 (see get_dataset()).
New default method in hv_approx(). Computation is now done in C, so it is much faster.
hv_contributions() is much faster for 2D inputs.

hypervolume() now uses the HV3D⁺ algorithm for the 3D case and the HV4D⁺ algorithm for the 4D case. For dimensions larger than 4, the recursive algorithm uses HV4D⁺ as the base case, which is significantly faster.
read_datasets() is significantly faster for large files.
is_nondominated() and filter_dominated() are faster for 3D inputs.
New function: hv_contributions().
New online datasets: test2D-200k.inp.xz and ran.1000pts.3d.10 (see get_dataset()).

New function: largest_eafdiff().
New class: RelativeHypervolume.
New dataset tpls50x20_1_MWT.csv.
Extended example Computing Multi-Objective Quality Metrics.
vorobT() and vorobDev() were renamed to vorob_t() and vorob_dev() to follow Python convention.
get_dataset_path() and get_dataset() can download large datasets from a remote repository.

Improved example Using moocore with Pandas to work in Pandas version >= 2.
Changed behavior of apply_within_sets(). The previous behavior could lead to subtle bugs.

New: Hypervolume: Object-oriented API for hypervolume indicator.
New: apply_within_sets(): Utility function to apply operations to individual datasets.
New: is_nondominated_within_sets(): Utility function to identify nondominated points within sets.
New example using pandas.DataFrame in Using moocore with Pandas.
Fix bug in normalise() when the input is pandas.DataFrame or some other non-contiguous array.